Keegan Hines is the Vice President of Machine Learning at ArthurAI, where he leads product development on Arthur’s enterprise-scale platform for trustworthy deployment of ML models. He is passionate about ensuring that ML technologies are used in a way that is interprtable, safe, and fair. He has previously led data science teams in a variety of industries including financial services, cybersecurity and educational technology. He is also an Adjunct Assistant Professor at Georgetown University, where he teaches graduate courses on statistics and machine learning methodologies. Additionally, Keegan is the co-founder and chair of the Conference on Applied Machine Learning for Information Security (CAMLIS), an international conference that brings together practitioners and researchers working at the intersection of machine learning and cybersecurity and defense. Keegan has a PhD in Neuroscience from the University of Texas and a BS in Physics from Washington and Lee University.Keegan Hines is the Vice President of Machine Learning at ArthurAI, where he leads product development on Arthur’s enterprise-scale platform for trustworthy deployment of ML models. He is passionate about ensuring that ML technologies are used in a way that is interprtable, safe, and fair. He has previously led data science teams in a variety of industries including financial services, cybersecurity and educational technology. He is also an Adjunct Assistant Professor at Georgetown University, where he teaches graduate courses on statistics and machine learning methodologies. Additionally, Keegan is the co-founder and chair of the Conference on Applied Machine Learning for Information Security (CAMLIS), an international conference that brings together practitioners and researchers working at the intersection of machine learning and cybersecurity and defense. Keegan has a PhD in Neuroscience from the University of Texas and a BS in Physics from Washington and Lee University.